### Normalization of the Estimates

In multidimensional scaling models, the parameter estimates are not uniquely determined; the estimates can be transformed in various ways without changing their badness of fit. The initial and
final estimates from PROC MDS are, therefore, normalized (unless you specify the NONORM option) to make it easier to compare
results from different analyses.

The configuration always has a mean of 0 for each dimension.

With the COEF=IDENTITY option, the configuration is rotated to a principal-axis orientation. Unless you specify the LEVEL=ABSOLUTE
option, the entire configuration is scaled so that the root-mean-square element is 1, and the transformations are adjusted
to compensate.

With the COEF=DIAGONAL option, each dimension is scaled to a root-mean-square value of 1, and the dimension coefficients are
adjusted to compensate. Unless you specify the LEVEL=ABSOLUTE option, the dimension coefficients are normalized as follows.
If you specify the CONDITION=UN option, all of the dimension coefficients are scaled to a root-mean-square value of 1. For
other values of the CONDITION= option, the dimension coefficients are scaled separately for each subject to a root-mean-square
value of 1. In either case, the transformations are adjusted to compensate.

Each dimension is reflected to give a positive rank correlation with the order of the objects in the data set.

For the LEVEL=ORDINAL option, if the intercept, slope, or power parameters are fitted, the transformed data are normalized
to eliminate these parameters if possible.